Finance

Document AI: invoices, forms and PDFs turned into structured data

Extraction with confidence scores and a review queue — because a wrong number entered automatically is worse than one entered by hand.

Industry: Finance
Platform: AI

Every business runs on documents somebody re-types: supplier invoices, purchase orders, application forms, identity proofs. It is the most automatable work in most organisations and the least automated. This reference build reads those documents into structured data — with a property that matters more than accuracy claims: every extracted field carries a confidence score, and anything below threshold goes to a review queue instead of into the ledger.

The challenge

Extraction demos are easy on clean documents. Reality is a photographed invoice at an angle, a scan with a staple through the total, a form filled in three different hands, and a supplier who redesigned their template last month. The deeper problem is that a wrong number entered automatically is more dangerous than one entered manually — nobody double-checks the machine.

Our strategy

Make uncertainty first-class. Every field carries a confidence score, thresholds are set per field by consequence — a total matters more than a description — and anything below goes to a human queue with the document and the extracted value side by side. Corrections feed back as training signal, so the queue shrinks over time rather than staying constant.

The solution

A pipeline that ingests documents from upload, email or a watched folder, classifies the type, extracts the fields that type implies, validates against business rules — totals that must add up, dates that must be plausible, references that must exist — and posts clean records into the destination system. Anything uncertain waits in a review queue where a person confirms in seconds rather than re-keying in minutes.

Results

What changed

Per-field

confidence

Uncertainty made visible

The system reports what it is unsure about rather than guessing quietly.
0

silent overwrites

Nothing enters unchecked

Below-threshold fields wait for a person before they reach your ledger.
Audit

trail retained

Original always kept

Every record can be traced back to the document it came from.

Key features

What we built

Multi-format ingestion

Uploads, email attachments or a watched folder — PDFs, scans and photos.
Schema-constrained extraction

Output is structured by document type, never free text to parse again.
Per-field confidence

Every value carries a score, with thresholds set by consequence.
Business-rule validation

Totals, dates and references checked before anything is written.
Human review queue

Uncertain fields confirmed in seconds, document and value side by side.
Originals retained

Every extracted record keeps its source document for audit.

Frequently asked questions

Is this a client case study?

No — it is a capability showcase of our document AI reference build. It carries no client name or live URL because it describes the architecture, not one deployment.

How accurate is it?

The honest answer is that accuracy depends entirely on your documents, which is why we do not publish a headline number. What we design for instead is knowing when it is unsure: per-field confidence with a review queue, so uncertain values never enter your system silently.

What document types can it handle?

Anything with a consistent shape: invoices, purchase orders, delivery notes, application forms, identity documents. Each type gets its own schema and validation rules.

Where does the data go?

Into whichever system should own it — your ERP, accounting package or a platform we build. Extraction is the front of a pipeline, not a destination.